Performance testing of multi-tier applications is critical to estimate maximum concurrency, throughput and response time the associated resources would support before reaching 100% utilization. Accurate theoretical modeling of performance under varying load is a useful basis for comparison with measured load testing data. Prediction models require an accurate estimate of service demands as inputs. However, service demands vary with increasing workloads that are not captured in conventional performance predicting models. Varying service demands are pathological in nature for multi-tier systems where utilization of the network resources and hardware such as Central Processing Unit (CPU) and storage disk do not scale proportionally with higher concurrency. Conventional Mean Value Analysis (MVA) models are based on constant service demands or an average value of service demands captured for varying workloads and therefore are unable to capture variation in service demands which leads to inaccurate prediction of throughput and response times at higher workloads.